METHODS: This secondary dataset analysis used data from the National Health and Morbidity Survey (NHMS) 2018. Data from 3914 participants were collected on elderly health in the Malaysian population. Sociodemographic characteristics were recorded. Smoking status was grouped as current smokers, former smokers, and non-smokers. A validated Malay language version of the Geriatric Depression Scale (M-GDS-14) was used to screen for depression among the elderly.
RESULTS: There was a significant association between smoking status with location, gender, employment status, marital status, ethnicity, education level, income, and depression. Current smokers are significantly higher in rural than urban areas. Among depressed participants, 65.7%, 17.1% and 17.2% were non-smokers, former smokers and current smokers, respectively. Multiple logistic regression showed that single (unmarried/separated/ divorced/widowed) participants were more likely to be depressed compared to married participants (AOR=1.68; 95% CI: 1.16-2.43). Whilst unemployed participants were more likely to be depressed than those who were employed (AOR=1.72; 95% CI: 1.22-2.44). Other Bumiputras were more likely to have depression compared to Malay, Chinese and Indian participants. Participants without formal education were more likely to be depressed compared to those having tertiary education. These participants have a 2-fold increased risk of depression (AOR=2.13; 95% CI: 1.02-4.45). Participants whose monthly salaries were <2000 MYR (AOR=3.67; 95% CI: 1.84-7.31) and 1000-1999 MYR (AOR=2.71; 95% CI: 1.23-5.94) were more likely to have depression compared with those who had received ≥3000 MYR. Ever smokers were more likely to be depressed than non-smokers (AOR=1.68; 95% CI: 1.23-2.29).
CONCLUSIONS: Elderly Malaysians are indeed at risk of developing depression particularly if they had ever smoked. Public health awareness and campaigning are pertinent to disseminate these outcomes in order to spread the awareness associated with smoking-related depression.
DESIGN: A population-based cross-sectional study.
SETTING: 13 states and 3 Federal Territories in Malaysia.
PARTICIPANTS: A total of 3966 adults aged 60 years and above were extracted from the nationwide National Health and Morbidity Survey (NHMS) 2018 data set.
PRIMARY OUTCOME MEASURES: Multimorbidity was defined as co-occurrence of at least two known chronic non-communicable diseases in the same individual. The chronic diseases included hypertension, type 2 diabetes mellitus, dyslipidaemia and cancer.
RESULTS: The prevalence of multimorbidity among Malaysian older adults was 40.6% (95% CI: 37.9 to 43.3). The factors associated with multimorbidity were those aged 70-79 years (adjusted OR (AOR)=1.30; 95% CI=1.04 to 1.63; p=0.019), of Indian (AOR=1.69; 95% CI=1.14 to 2.52; p=0.010) and Bumiputera Sarawak ethnicities (AOR=1.81; 95% CI=1.14 to 2.89; p=0.013), unemployed (AOR=1.53; 95% CI=1.20 to 1.95; p=0.001), with functional limitation from activities of daily livings (AOR=1.66; 95% CI=1.17 to 2.37; p=0.005), physically inactive (AOR=1.28; 95% CI=1.03 to 1.60; p=0.026), being overweight (AOR=1.62; 95% CI=1.11 to 2.36; p=0.014), obese (AOR=1.88; 95% CI=1.27 to 2.77; p=0.002) and with abdominal obesity (AOR=1.52; 95% CI=1.11 to 2.07; p=0.009).
CONCLUSION: This study highlighted that multimorbidity was prevalent among older adults in the community. Thus, there is a need for future studies to evaluate preventive strategies to prevent or delay multimorbidity among older adults in order to promote healthy and productive ageing.
METHODS: A cross sectional survey was conducted among staff from a tertiary education centre. Subjects were contacted to ascertain their medical history. A total of 320 subjects were interviewed and 195 subjects were eligible and subsequently recruited on a suitable date for taking blood and administration of the questionnaires. The subjects completed questionnaires pertaining to demographic details and coping styles. Pearson's correlation coefficient was used to measure the strength of association between lipid profile and coping styles.
RESULTS: Majority of the subjects were non-academic staff (60.0%), female (67.2%), Malay (91.8%), married (52.3%) and educated until Diploma level (34.9%). Academic staff scored significantly higher mean scores in task-oriented coping styles (Mean = 64.12). Non-academic staff scored significantly higher mean scores in emotion (Mean = 48.05) and avoidance-oriented coping styles (Mean = 57.61). Malay subjects had significantly higher mean scores in emotion (Mean = 47.14) and avoidance-oriented coping styles (Mean = 55.23). Non-malay subjects (Mean = 66.00) attained significantly higher mean scores in task-oriented coping styles. Single/divorced/widowed individuals scored significantly higher mean scores in emotion (Mean = 48.13) and avoidance-oriented coping styles (Mean = 56.86). There was a significant negative correlation between TC (r = -0.162) and LDL (r = -0.168) with avoidance-oriented coping styles (p = 0.023, p = 0.019 respectively).
CONCLUSION: Avoidance-oriented coping style was more likely to engender favourable lipid profile. Hence, assessment of coping styles would certainly assist health care practitioners in predicting subjects who would be at a greater risk of developing cardiovascular diseases.